PB2504: USE OF CONSUMER WEARABLES TO MONITOR AND PREDICT PAIN IN PATIENTS WITH SICKLE CELL DISEASE
Authors: Caroline Vuong, Kumar Utkarsh, Rebecca Stojancic, Arvind Mallikarjunan, Olivia Fernandez, Karin Fijnvandraat, Nirmish Shah
Journal: HemaSphere (2023)

Topic: 26. Sickle cell disease Background: Sickle cell disease (SCD) is associated with increased morbidity and mortality. Episodes of acute and severe pain known as vaso-occlusive crises (VOC) are the most common cause for hospital admission. Mobile health (mHealth) has developed promising, patient-friendly, minimally invasive tools to monitor patients remotely. Our previous work has leveraged data from mHealth apps and wearable devices to evaluate several machine learning (ML) models to accurately predict pain scores in patients with SCD currently admitted for VOC to the SCD Day Hospital. Aims: To evaluate the feasibility of monitoring pain during hospital admission and for 30 days post-discharge to refine the development of ML models to predict pain in patients with SCD. Methods: Patients with SCD aged 18 years and above, who were admitted for a VOC to the SCD Day Hospital or to Duke University Hospital, were eligible for this study. All patients were followed for 30 days following discharge. Following informed consent, patients were provided: 1) a mobile app (Nanbar); and 2) an Apple Watch. Patients were instructed to report their pain at least once daily within the Nanbar app. Patients were asked to continuously wear the Apple Watch, removing only to charge. Physiological data collected by the Apple Watch included heart rate, heart rate variability, step count and calories. These data were associated with self-reported pain scores to fit 5 different ML classification models for the pain prediction. The performance of the ML models is evaluated by the following metrics: accuracy, F1-score, root mean squared error (RMSE) and area under the ROC curve (AUC). Results: Nineteen patients were included in this study from April through June 2022. The median age at time of inclusion was 30 years (IQR:22-34). The majority of the patients had genotype HbSS (68%) and all were Black or African American. Six patients were readmitted at least once within 30 days after discharge (32%). This preliminary dataset consisted of 1480 data points. After micro-averaging due to the imbalanced dataset, the performance of all the models were very similar. The metrics of the best performing model, the random forest model, were: micro-averaged accuracy: 0.89, micro-averaged F1-score: 0.50, RMSE: 1.52, AUC: 0.83. There was no correlation between any of the data elements recorded by the Apple Watch. Our random forest model was able to accurately predict higher pain scores not only for patients who were admitted to the hospital, but also for patients after discharge. Summary/Conclusion: The consumer wearable Apple Watch was a feasible method to collect physiologic data and provided accuracy in prediction of higher pain scores. We believe mHealth efforts can provide valuable insights for patient monitoring and pain prediction for patients with SCD. The next step will involve the prediction of readmission within 30 days after discharge using machine learning techniques.Keywords: Machine learning, Sickle cell anemia, Sickle cell disease